It is worth mentioning that DeformDETR [1]} is a special case in the detection transformer family, which shows comparable data efficiency to Sparse RCNN. Our conclusions drawn from the Sparse RCNN to DETR model transformation can also explain DeformDETR’s data efficiency. Specifically, multi-scale deformable attention samples sparse features from local regions of the image and utilizes multi-scale features. The prediction of the model is relative to the initial reference points. Thus, all three key factors are satisfied in DeformDETR, though it was not intended to be data-efficient on small-size datasets.
